.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_cinn_builder.py:
Run model by using CinnBuilder API
=========================================================
In this tutorial, we will introduce the ways to build and run a model using CinnBuilder APIs.
.. code-block:: python
import cinn
from cinn import frontend
from cinn import common
import numpy as np
# sphinx_gallery_thumbnail_path = './paddlepaddle.png'
Define the CinnBuilder.
-----------------------------
Using CinnBuilder is a convenient way to build a model in CINN. You can build and run
a model by invoking CinnBuilder's API as following.
:code:`name`: the ID of CinnBuilder
.. code-block:: python
builder = frontend.CinnBuilder(name="add_conv")
Define the input variable of the model.
---------------------------------------------
The input variable should be created by create_input API. Note that the variable
here is just a placeholder, does not need the actual data.
:code:`type`: the data type of input variable, now support `Void`, `Int`, `UInt`,
`Float`, `Bool` and `String`, the parameter is the type's bit-widths, here the
data type is `float32`.
:code:`shape`: The shape of the input variable, note that here does not support
dynamic shape, so the dimension value should be greater than 0 now.
:code:`id_hint`: the name of variable, the defaule value is `""`
.. code-block:: python
a = builder.create_input(
type=common.Float(32), shape=(1, 24, 56, 56), id_hint="A")
b = builder.create_input(
type=common.Float(32), shape=(1, 24, 56, 56), id_hint="B")
c = builder.create_input(
type=common.Float(32), shape=(144, 24, 1, 1), id_hint="C")
Build the model by using CinnBuilder API
---------------------------------------------
For convenience, here we build a simple model that only consists of add and conv
operators. Note that you can find the operator's detailed introduction in another
document, we won't go into detail here.
Generally, the API in CinnBuilder is fine-grained operator, in other words,
the algebra or basic DL operator.
.. code-block:: python
d = builder.add(a, b)
res = builder.conv(d, c)
Set target
---------------------
The target identified where the model should run, now we support
two targets:
:code:`DefaultHostTarget`: the model will running at cpu.
:code:`DefaultNVGPUTarget`: the model will running at nv gpu.
.. code-block:: python
if common.is_compiled_with_cuda():
target = common.DefaultNVGPUTarget()
else:
target = common.DefaultHostTarget()
print("Model running at ", target.arch)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Model running at Arch.X86
Generate the program
---------------------
After the model building, the `Computation` will generate a CINN execution program,
and you can get it like:
.. code-block:: python
computation = frontend.Computation.build_and_compile(target, builder)
Random fake input data
-----------------------------
Before running, you should read or generate some data to feed the model's input.
:code:`get_tensor`: Get the tensor with specific name in computation.
:code:`from_numpy`: Fill the tensor with numpy data.
.. code-block:: python
tensor_data = [
np.random.random([1, 24, 56, 56]).astype("float32"),
np.random.random([1, 24, 56, 56]).astype("float32"),
np.random.random([144, 24, 1, 1]).astype("float32")
]
computation.get_tensor("A").from_numpy(tensor_data[0], target)
computation.get_tensor("B").from_numpy(tensor_data[1], target)
computation.get_tensor("C").from_numpy(tensor_data[2], target)
Run program and print result
-----------------------------
Finally, you can run the model by invoking function `execute()`.
After that, you can get the tensor you want by `get_tensor` with tensor's name.
.. code-block:: python
computation.execute()
res_tensor = computation.get_tensor(str(res))
res_data = res_tensor.numpy(target)
# print result
print(res_data)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[[[[11.571602 10.037912 12.110612 ... 11.803285 10.4503975
12.39268 ]
[12.118155 11.331117 11.2125 ... 10.547782 12.086964
10.297881 ]
[11.0286255 10.750516 11.5008745 ... 12.0907955 12.7944145
12.78049 ]
...
[10.9749365 10.080397 10.170232 ... 11.073846 13.112092
11.807571 ]
[11.376714 13.390822 10.701132 ... 8.375713 12.820743
12.455052 ]
[13.08021 10.476735 12.054475 ... 11.040751 10.412604
12.462891 ]]
[[11.715233 10.517147 11.598742 ... 11.481009 9.9403715
11.722999 ]
[11.467173 10.645823 11.332581 ... 9.847761 10.552914
10.138514 ]
[10.465738 9.978148 10.484111 ... 10.257616 12.233998
12.2045965]
...
[10.829692 9.717082 9.959618 ... 10.864071 13.141303
11.720429 ]
[11.869147 12.892423 11.600269 ... 9.288053 13.1629715
11.902034 ]
[12.196988 10.507401 12.522007 ... 11.306245 11.8908
11.879142 ]]
[[ 7.0207853 6.780528 7.1823373 ... 7.7550783 7.077457
8.6607685]
[ 7.534683 7.4253626 7.140933 ... 6.2216945 8.172223
6.5323215]
[ 7.563627 7.8048253 7.6544023 ... 7.292347 8.270231
8.02856 ]
...
[ 7.4650517 6.3309436 6.0289545 ... 7.835066 8.898898
7.6010804]
[ 8.542815 8.381237 6.937894 ... 6.03162 8.595205
8.731508 ]
[ 8.340959 6.5326405 7.241155 ... 7.484935 7.0709844
7.9060907]]
...
[[11.478321 10.483002 10.756042 ... 11.76803 10.395924
12.380386 ]
[12.15006 11.723205 11.261727 ... 11.094739 12.16321
11.7729025]
[10.40236 11.365674 12.381306 ... 12.539725 13.496915
13.967277 ]
...
[12.309769 10.177728 10.188444 ... 12.48512 13.499703
12.724175 ]
[13.344845 13.365545 12.548767 ... 9.05944 15.144086
13.429598 ]
[15.571053 11.193485 13.457433 ... 13.745621 12.467106
13.37518 ]]
[[13.315583 13.315888 12.039522 ... 11.5344515 12.055772
13.386152 ]
[12.634843 11.162848 12.577147 ... 11.380696 12.809536
12.337452 ]
[12.297415 11.702893 12.784704 ... 12.3519335 14.45426
13.621389 ]
...
[12.130316 11.363695 11.692377 ... 12.95829 14.201069
12.42412 ]
[13.981301 13.729186 12.080306 ... 9.493916 14.731629
13.593263 ]
[14.004305 10.771607 12.4516535 ... 14.381553 12.467554
13.803441 ]]
[[13.910039 14.278668 13.928883 ... 14.040823 12.109072
14.80458 ]
[13.744701 13.308964 14.76345 ... 13.136091 14.855599
14.121593 ]
[14.864944 12.936687 15.485008 ... 14.28767 16.60473
15.257153 ]
...
[13.914206 11.639483 12.371835 ... 14.61134 16.152748
14.045977 ]
[16.510437 16.021467 13.342856 ... 10.186195 16.333027
15.138903 ]
[15.972713 12.521988 15.687452 ... 15.2209425 14.478452
13.770096 ]]]]
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 3.108 seconds)
.. _sphx_glr_download_tutorials_cinn_builder.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: cinn_builder.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: cinn_builder.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_